Spatial models to account for variation in observer effort in bird atlases

Area of Science:

• Ecology
• Biodiversity
• Conservation Biology

Background:

• Bird atlas projects generate valuable species distribution data.
• Variations in observer effort can introduce bias.
• Accurate analysis of atlas data is essential for conservation.

Purpose of the Study:

• To evaluate the impact of observer effort variation on bird atlas data analysis.
• To demonstrate the utility of conditional autoregressive (CAR) models for analyzing grid-based atlas data with varying effort.
• To assess the influence of CAR models on distribution change assessments.

Main Methods:

• Utilized varying proportions of training data to test CAR model robustness with incomplete data.
• Applied CAR models incorporating observer effort and landscape covariates.
• Evaluated model performance on atlas data from Pennsylvania and West Virginia.

Main Results:

• CAR models, with or without landscape covariates, provided robust species distribution predictions even with sparse data.
• These models successfully accounted for observer effort variations between and within atlas projects.
• Incorporating observer effort significantly impacted the assessment of distribution changes over time.

Conclusions:

• Accounting for observer effort variation is critical for reliable bird atlas data analysis.
• Conditional autoregressive (CAR) models offer a simple, efficient framework for analyzing bird atlas data with varying observer effort.
• CAR models enhance the accuracy of distribution change assessments, aiding conservation efforts.